While great progress has been made recently in automatic image manipulation,<br>it has been limited to object centric images like faces or structured scene<br>datasets. In this work, we take a step towards general scene-level image<br>editing by developing an automatic interaction-free object removal model. Our<br>model learns to find and remove objects from general scene images using<br>image-level labels and unpaired data in a generative adversarial network (GAN)<br>framework. We achieve this with two key contributions: a two-stage editor<br>architecture consisting of a mask generator and image in-painter that<br>co-operate to remove objects, and a novel GAN based prior for the mask<br>generator that allows us to flexibly incorporate knowledge about object shapes.<br>We experimentally show on two datasets that our method effectively removes a<br>wide variety of objects using weak supervision only<br>